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Genome Biology 2008, 9:R52
Open Access
2008Manikandanet al.Volume 9, Issue 3, Article R52
Method
Functionally important segments in proteins dissected using Gene
Ontology and geometric clustering of peptide fragments
Karuppasamy Manikandan
*†
, Debnath Pal
*‡¶
,
Suryanarayanarao Ramakumar
*†‡
, Nathan E Brener
§
, Sitharama S Iyengar
§

and Guna Seetharaman
§
Addresses:
*
Bioinformatics Centre, Indian Institute of Science, Bangalore 560012, India.
†
Department of Physics, Indian Institute of Science,
Bangalore 560012, India.
‡
Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560012, India.
§
Department
of Computer Science, Louisiana State University, Baton Rouge, LA 70803, USA.

¶
Main correspondence.
Correspondence: Debnath Pal. Email: Suryanarayanarao Ramakumar. Email:
© 2008 Karuppasamy et al.; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License ( which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Identifying functionally important protein segments<p>A geometric clustering algorithm has been developed to dissect protein fragments based on their relevance to function.</p>
Abstract
We have developed a geometric clustering algorithm using backbone φ,ψ angles to group
conformationally similar peptide fragments of any length. By labeling each fragment in the cluster
with the level-specific Gene Ontology 'molecular function' term of its protein, we are able to
compute statistics for molecular function-propensity and p-value of individual fragments in the
cluster. Clustering-cum-statistical analysis for peptide fragments 8 residues in length and with only
trans peptide bonds shows that molecular function propensities ≥20 and p-values ≤0.05 can dissect
fragments within a protein linked to the molecular function.
Background
Analysis of the protein fold reveals only a part of the informa-
tion contained in the protein structure, whereas analysis of
protein structure as an assembly of peptide fragments in a
defined order provides additional information with respect to
certain desired features [1-4]. Simple analysis of the distribu-
tion of fragments and their recurrence in protein structures
helps to better understand the underlying rules of their for-
mation [5,6]. Since structure is better conserved during evo-
lution than sequence, structural similarities help to more
effectively identify remote evolutionary relationships. They
can be reliably used in identifying functional sites as well as
functions of proteins on a larger scale [7].
Protein annotation efforts benefit immensely from knowl-
edge of functional signatures in primary, secondary and terti-

ary structures. Calcium-binding motifs, such as the EF hand
[8] and zinc-binding [9], chitin-binding [10] and ATP/GTP-
binding motifs [11], are well known examples of fragment-
based functional three-dimensional structural signatures in
proteins. Interestingly, however, only a few fragment-based
geometric clustering methods exist that can automatically
identify motifs and relate them to function [12]. The lack of
such methods is mainly due to the large computation time
required to perform the studies. To bypass such difficulties,
some authors have used clustering of the secondary structure
patterns [13] or symbolic representation of structural frag-
ments [14-16] to relate protein fragments to function. In most
cases the studies are limited to describing the known rele-
vance of fragments in inferring biochemical function. This is
in contrast to a large number of methods developed for find-
ing functionally significant three-dimensional motifs formed
from non-contiguous amino acids in the polypeptide chain.
Structure-based residue/chemical group clustering in
Published: 10 March 2008
Genome Biology 2008, 9:R52 (doi:10.1186/gb-2008-9-3-r52)
Received: 30 November 2007
Revised: 24 February 2008
Accepted: 10 March 2008
The electronic version of this article is the complete one and can be
found online at />Genome Biology 2008, 9:R52
Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.2
combination with multiple sequence alignment has been fre-
quently used for this purpose [17-19]. Numerous studies also
exist where sequence information alone has been used to
assess function [20]. One such recent study [21] identifies

function-associated loops in proteins using Gene Ontology
(GO) [22] molecular function (MF) terms. In this case, the
starting information was structure, and from that the
sequence pattern was derived.
Fragments derived from structure-based sequence signatures
offer an attractive way to annotate protein function because of
their applicability to both sequences and structures with
unknown function. In this paper we have used a clustering
algorithm based on backbone φ,ψ torsion angles to find con-
formationally similar peptide fragments of different lengths
from the FSSP library [23], which contains a large number of
proteins with distinct folds. This algorithm is derived from
the demographic clustering technique used in data mining
applications [24]. A distinct feature of the clustering proce-
dure ensures that the clusters are formed with their centers at
the locations with the densest distributions of points in the
torsion angle space. The clusters show that protein fragments
extremely divergent in sequence can adopt similar conforma-
tions. Yet within the clusters, GO MF terms associated with
the fragments (as derived from the Protein Data Bank (PDB)
annotation) can be over-represented, and identified by a sta-
tistically significant distribution of propensity values, high-
lighting the primary importance of the fragment to
biochemical function. Geometric and sequence signatures
derived from this work will be useful in assessing proteins
with unknown function. Protein modeling, design and engi-
neering experiments would also benefit from this work.
Results
Fragments used in clustering
The clustering algorithm was applied to 2,619 PDB [25]

chains culled from the FSSP database, each representing a
unique fold as given in the DALI domain dictionary (see Addi-
tional data file 1 for PDB details). We clustered peptide frag-
ments of various lengths that contained only trans peptide
bonds; Table 1 lists the statistics for lengths 5-24, which we
used for this study. A maximum of 455,305 fragments with a
length of 5 residues were generated from all the PDB chains;
this number decreased linearly with increasing fragment
length (FL; number of fragments = (-13,243 × FL) + 468,104;
R
2
= 0.99). The largest number of clusters with 2 or more
fragments were generated for the data set including frag-
ments with a FL of 14 (data set FL14; 26,778 clusters). The
number of clusters varies non-linearly with increasing FL
(Figure 1a). For the FL5 data set, the number of clusters, as
well as the number of singletons left unclustered, is low. With
increasing FL up to 14, the number of clusters increases, as
does the number of singletons left unclustered. As a result,
the sequence diversity of fragments is high in low FL clusters
compared to high FL clusters. Indeed, the largest cluster size
for at a FL of 5 constitutes 27% of the total FL5 data set (Table
1). The fraction of total data points included in the largest
cluster decreases exponentially with increasing FL (Figure
1b). When we use all clusters with 2 or more members, 98.8%
of the total fragments in the database are clustered for trans
FL5. The coverage progressively decreases to below 40% for
trans FL20 or more. If we consider only clusters with 10 or
more fragments, at least 40% coverage can be achieved with
FLs of only 14 or less. The compactness of clusters also

increases with increasing FL (Table 1, last column). Repre-
sentative distributions for FL8 and FL16 across all clusters
also show similar trends (Additional data file 2). These sug-
gest that the optimal range for scanning biologically relevant
motifs is between FLs of 8 and 14, where we can choose large
clusters ignoring short fragments and also eliminate a large
number of clusters with just a few members. To identify what
cluster size is significant for statistical analysis, we plotted the
normalized frequency of occurrence of the clusters from indi-
vidual FL data sets (data not shown) against the rank of clus-
ters in terms of size. The distribution follows a power-law and
the distribution of clusters of both FL8 and FL16 with ten or
more fragments follow Zipf's law, suggesting their suitability
for data mining analysis [26].
Information content of clustered fragments
Before performing any analysis with the clusters, we also
checked their distribution of average information content
(sequence entropy). As can be seen in Figure 1c, for a given
cluster, the more the fragment pairs have the same residues
at identical positions, the lower the information content. The
major peaks of the distribution of information content
derived from geometric clusters are at values higher than 1.0
for both FL8 and FL16. Some of the clusters with large infor-
mation content (>2.0) have an especially large number of
fragments with extensive sequence diversity. Further analysis
showed that only clusters with less than ten fragments, which
also did not conform to Zipf's law, had information contents
<1.0. A general survey of FL8 clusters with 10 or more frag-
ments showed only 592 of them having at least one position
with greater than 80% amino acid conservation. Notably,

97% of the conserved residues were found to be Gly and the
remaining conserved residues are Cys, Asp, Lys and Ser in
decreasing order. However, the overall distribution of amino
acids between the clustered fragments and the total data set
of proteins was found to be similar, indicating the data set
used for this study is unbiased. Analysis with FL16 clusters
essentially gave similar results (Figure 1c), with Gly again
being the most conserved residue followed by Asp and Lys.
Identification of functionally important fragments
In order to identify the functional relevance of the fragments
in clusters, we investigated the GO MF terms of the fragments
in clusters mapped from their original PDB annotations. It
was found that many of the functionally significant structural
motifs grouped into distinct clusters, for example, helix-turn-
helix DNA binding, ATP/GTP binding P-loop, iron binding
Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.3
Genome Biology 2008, 9:R52
motifs and so on. However, we did not find any cluster that
had only a single GO term across all clustered fragments. This
was because in many cases similar GO terms from different
levels in the GO graph were present as the annotated term
(Figure 2). Therefore, to cluster GO terms in order to identify
functionally significant fragments within the cluster that
relate directly to the function of the protein, it was important
to map the original GO MF (as available from the PDB) terms
of the fragments to a specific level in the Ontology graph. It
should be noted that a GO term can have multiple levels
depending on how its path to the root GO term in the Ontol-
ogy graph is traced. The 678 and 657 unique GO MF terms
obtained from the PDB for clustered fragments of FL8 and

FL16, respectively, were used for mapping the GO terms to
minimum ontology levels of 3, 4, and 5. In some cases, how-
ever, a fragment originally PDB annotated at level 3 could not
be represented at a deeper level 5 based on the Ontology
graph. Therefore, although we have done our calculations for
all the levels, because of poorer coverage at deeper levels we
discuss the details of results available from only level 3.
The counts of GO MF terms mapped at levels 3, 4, and 5 for
fragments in each cluster were used to calculate the propen-
sity of occurrence of the unique GO terms in each cluster. The
distributions of propensity values are shown in Figure 3. It
can be seen that the fraction of fragments with propensity
values 0-4 is higher at level 3 for both FL8 and FL16, decreas-
ing gradually for levels 4 and 5. The occurrence of propensity-
values shows a peak between 1 and 2 and follows a normal dis-
tribution with an extended tail beyond propensity value 5 or
more. Till this point a Gaussian function can be fit to all the
curves with least-square (R
2
) values >0.9. Interestingly, a
propensity value different from 1 itself points to its statistical
significance; but by plotting the distribution we further find
that fragments with GO terms with propensity values beyond
5 are enriched to have a significant functional relevance.
Using the hypergeometric distribution, we further confirmed
the statistical significance by calculating p-values for FL8 and
FL16 fragments for all GO terms mapped to levels 3, 4 and 5.
For all GO terms, when we examine the distribution of p-val-
ues against propensity, we clearly see that for p-values ≤0.05
the propensity values are always ≥20 (data not shown).

Therefore, we retained these statistically significant high pro-
pensity fragments for further analysis.
Since fold is intimately related to function, we also asked if we
get similar results when we repeat our calculations, replacing
the GO terms with CATH database [27] identifiers for the pro-
teins. We mapped GO-based and CATH-based (four level
hierarchy) propensities for individual fragments in our data
set, wherever both GO term and CATH identifiers were
present for the protein. The results showed poor correlation
between CATH-based and GO-based propensities (correla-
tion coefficient = 0.13). When we considered only fragments
with GO-based propensity ≥20, the correlation improved
marginally to 0.18. This indicated that the information avail-
able from fold-based propensity and GO term-based propen-
sity is distinct.
Relation to PROSITE patterns
To verify if indeed GO-based propensity indicated meaning-
ful inference of functional relevance, we selected 1,797 frag-
ments with propensity values ≥20 from the FL8 clusters
(Table 2; see Materials and methods for selection protocol).
The relevance of a fragment to function was probed by exam-
ining if the fragment overlaps with a PROSITE [28] pattern.
The criteria of presence/absence, overlap/non-overlap of
PROSITE patterns allowed grouping into four categories for
each protein fragment. The first group (Group 1) is where the
protein does not have any PROSITE signature and possibly
the fragment derived sequence pattern can be used as a new
Plot showing (a) the variation of the number of clusters (≥2 fragments) with fragment length, (b) the variation of the largest cluster size (expressed as a fraction of the total number of clustered fragments in the database) with fragment length, and (c) the distribution of average information content of all clustersFigure 1
Plot showing (a) the variation of the number of clusters (≥2 fragments)
with fragment length, (b) the variation of the largest cluster size

(expressed as a fraction of the total number of clustered fragments in the
database) with fragment length, and (c) the distribution of average
information content of all clusters. Data are plotted for clusters with ≥10
fragments.
1.0 1.5 2.0 2.5 3.0
0
20
40
60
80
100
FL8
FL16
Random_FL8
Random_FL16
Normalized frequency
Average information content
y = 0.53 e
(-x/7.9)
R
2
= 0.99

6 8 10 12 14 16 18 20 22 24
0
0.1
0.2
0.3
Largest-cluster size
Fragment length

6 8 10 12 14 16 18 20 22 24
10k
20k
30k
Number of clusters
(a)
(b)
(c)
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Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.4
regular expression signature pattern. In the second group
(Group 2), the protein has one or more PROSITE pattern(s),
but the sequence of the fragment does not overlap with them.
In the remaining two cases (Groups 3 and 4), the PROSITE
pattern either overlaps partly or contains the sequence of the
fragment. As can be seen, a large number of patterns were
predicted from Groups 1 and 2, which constitutes new infor-
mation. To establish the functional importance of these frag-
ments, we randomly picked them for literature review. All the
randomly chosen fragments we reviewed were identified to be
functionally important, representative examples [29-42] of
which are listed in Table 3. The p-values were ≤0.05 in all
cases, indicating statistical significance. These suggested that
a GO MF based analysis of propensities and associated p-val-
ues allows a strong relation of fragments to relevant biochem-
ical functions. While reviewing the literature we checked if
the relevance of a fragment to the function of the protein was
evident from the text, explaining a direct relationship to
experimentally determined known functional sites in pro-
teins. A recheck of the results with FL16 fragments using level

3 GO MF terms showed occasional overlap with FL8 results,
indicating that results common to both the fragment lengths
may be suitably used to enhance the confidence of interpreta-
tion, wherever possible. In general, the number of high pro-
pensity fragments for a protein may vary widely, but larger
proteins tend to have more of them.
Examples of sequence-structure patterns
Group 1: NS3 protease
No PROSITE sequence signature pattern is available for NS3
protease (PDB: 1df9A
[43]). It was found that the first and
third ranked fragments derived from level 3 GO propensity
calculations encompass residues 132-141 and contribute resi-
dues to the binding pocket of the protease (Table 4). In par-
ticular, it has been shown [43] that Pro132 and Gly133 make
van der Waals interactions with the P2' region of the Bow-
man-birk inhibitor while Ser135 and Ser163 participate in
side-chain polar interactions with the inhibitor's polar atoms
at Lys20 in the P1 site (Figure 4, Group 1). A fragment con-
taining residue 163 (156-163) was found with a lower propen-
sity value. It is interesting to note that residues 96-103, which
represent fragments showing the second ranked propensity,
form a scaffold for the active site, which corroborates its def-
inite structural significance (p-values ≤0.05).
Table 1
Overall statistics of generated clusters from all trans fragments
FL Total fragments Total number of clusters with >2 fragments
(% fragments clustered)
Largest cluster
Size (% of total fragments) Compactness* (SD)

5 455,305 5,544 (98.8) 121,220 (27) 2.92 (1.8)
6 446,479 8,466 (97.3) 106,020 (24) 2.62 (1.5)
8 429,793 15,617 (92.1) 79,646 (19) 2.23 (1.2)
10 414,207 22,120 (83.7) 58,150 (14) 2.0 (1.0)
12 399,615 26,228 (72.9) 40,935 (10) 1.81 (0.87)
14 385,866 26,778 (61.2) 28,313 (7) 1.68 (0.77)
16 369,760 25,455 (50.8) 19,469 (5) 1.56 (0.70)
18 360,537 23,302 (41.2) 13,519 (4) 1.45 (0.63)
20 348,824 21,079 (33.4) 9,551 (3) 1.37 (0.59)
22 337,679 18,646 (28.8) 6,804 (2) 1.29 (0.55)
24 327,010 16,132 (21.4) 4,966 (2) 1.22 (0.52)
*(Average of the distances of all fragments in a cluster from its center)/(2 × FL). SD, standard deviation.
Figure depicting the concept of the GO directed acyclic graph for PDB entry 1wohFigure 2
Figure depicting the concept of the GO directed acyclic graph for PDB
entry 1woh
. Each node is represented by a unique GO MF term
(GO:0003674, molecular function; GO:0003824, catalytic activity;
GO:0005488, binding; GO:0016787, hydrolase activity; GO:0016810,
hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds;
GO:0016813, hydrolase activity, acting on carbon-nitrogen (but not
peptide) bonds, in linear amidines; GO:0019239, deaminase activity;
GO:0043167, ion binding; GO:0043169, cation binding; GO:0046872,
metal ion binding). The level of each GO term is indicated in the round
text box. Note that the same GO term can have multiple levels depending
on how you trace the path to the root GO term. The terms depicted in
bold are annotated for the PDB in the GOA database [68]. A protein can
be represented at various GO levels by taking the parent GO terms of the
original PDB annotation.
GO:0003674
GO:0003824 GO:0005488

GO:0016787 GO:0019239
GO:0016810
GO:001681
2
2
3 3
4
GO:0043167
GO:0043169
GO:0046872
1
2
3
3
4
Ontology level
1
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Genome Biology 2008, 9:R52
Group 2: phosphatidylinositol kinase activity
In the protein (PDB: 1e7uA [44]) two PROSITE patterns
(PS00915, residues 691-705, and PS00916, residues 790-
810) describe the phosphatidylinositol 3-kinase and 4-kinase
(EC 2.7.1.153) signatures 1 and 2 (Table 4), respectively. The
top ranked fragment identified from our analysis (857:
TESLDLCL) forms a rigid linker that contributes residues to
the binding of ATP and/or inhibitors and are essentially in the
binding pocket of the protein [44] (Figure 4, Group 2). On one
end of this linker (872: TGDKIGMI), the backbone nitrogen
of Val882 makes important hydrogen bonding contacts.

Tyr867, which is part of two overlapping high propensity
fragments (861: DLCLLPYG), is critical to the binding of ATP
and the inhibitor molecules. Experimental analyses show
mutation at this position reduces lipid kinase activity to less
than 10% of the wild-type enzyme. The integrity of the cata-
lytic site is maintained by rigid packing around Tyr867, as
evident from a mutation study in a phosphatidylinositol 3-
kinase
γ
homolog, where a I963A modification completely
abolished the catalytic activity [44].
Groups 3 and 4: growth factor
β
3
Growth factor β3 (PDB: 1tgj [45]) is described by a PROSITE
pattern (PS00250) that corresponds to the transforming
growth factor beta (TGF) family. The second ranked fragment
identified at a level 3 propensity calculation starts at residue
27 and partly overlaps the PROSITE pattern (Table 4). The
fragment contains two functionally critical residues. Trp30
and Trp32 interact with the dioxane, which has structural
similarity to a carbohydrate moiety (Figure 4, Group 3). The
Trp residues are shown to be involved in carbohydrate recog-
nition [45]. It is noteworthy that the two Trp residues are
totally conserved in the known TGF families, implying that
these residues could be incorporated into the present
PROSITE signature pattern, which would in turn enhance the
functional prediction from the sequence. Other lower ranked
overlapping fragments starting at residue 22 span the whole
of the PROSITE pattern.

Mapping high propensity fragments in proteins, and
functional relevance
A protein can sometimes have many high propensity frag-
ments and be annotated with multiple GO terms, giving rise
Distributions of propensity values of GO MF terms computed in each clusterFigure 3
Distributions of propensity values of GO MF terms computed in each cluster. L3, L4, and L5 refer to ontology levels 3, 4 and 5, respectively.
0
5
10
15
20
25
30
35
0
5
10
15
20
25
30
35
40
45
>50
FL8 L3 FL8 L4 FL8 L5
FL16 L3 FL16 L4 FL16 L5
Normalized frequency
Propensity
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Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.6
to a peculiar situation while relating a fragment to its relevant
GO MF term. In our calculations, since the propensity is
derived after mapping the individual GO MF at a specific level
from the fragment, the reverse mapping may not be unique.
Therefore, although fragments may be of strong functional
relevance as indicated by propensity calculations, they may
not be uniquely identified with a specific MF. The possibility
of specific mapping of fragments to relevant function
increases as we perform our propensity calculations at deeper
GO levels of 4 or more. As a case study we examined PDB
entry 1woh
[30], with only two GO terms, GO:0016813 and
GO:0046872 (Figure 2). PDB entry 1woh
is a 305 residue
agmatinase binuclear manganese metalloenzyme. The
protein is without any PROSITE sequence pattern, yet a look
at the propensity mappings showed some interesting trends
(Figure 5). As can be seen from all propensity values ≥20
mapped to fragment start positions at different GO levels,
large parts of the protein are covered by high propensity frag-
ments, the coverage being more dense around conserved
regions, especially around the functionally important resi-
dues. It may be noted that the fragments derived from the
FL16 calculations occasionally overlap with the FL8 calcula-
tions at level 3. All fragments at level three are mapped
through GO:0016813. But on using level 4 for propensity cal-
culations, GO:00046872 could be mapped to only two func-
tionally relevant fragments, one of which includes Ser243,
which is a part of the active site. At level 5 no propensity

calculations could be made for the protein because the deep-
est level of GO:0016813 and GO:0046872 is 4. Therefore,
deeper level annotations are desirable for improved use of our
methodology. It should also be noted that FL8 and FL16
results (shown as triangles in Figure 5) do not always neces-
sarily overlap. Cases where they do not overlap occur where
the FL8 fragment is completely contained in a regular sec-
ondary structure (like an
α
-helix), while the longer FL16 frag-
ment starting around the same postion is long enough to
Table 2
The distribution of selected FL8-derived sequence patterns with propensity ≥20
Group number Occurrence of the sequence pattern Number of patterns/PDB entries
1 No PROSITE pattern for the protein 521/50
2 The sequence occurs outside the PROSITE pattern 838/106
3 The sequence is within the PROSITE pattern 364/76
4 The sequence overlaps with the PROSITE pattern 107/35
See Materials and methods for the method of selection.
Table 3
Details of arbitrarily chosen FL8 fragments with propensity ≥20 mapped from GO propensity calculations at level 3
GO MF Propensity PDB entry
[reference]*
Start
†
Functional description P-value
0004016 1,816 1azsA [34] 489 VC1 and IIC2 domain interface 0.0006
0019210 1,450 1jsuC
[35] 61 Highly conserved β hairpin from p27 interacting with Cdk2 and inhibiting the cyclin-
Cdk2 complex

0.0007
0000036 685 1t8kA
[33] 19 Part of ligand binding region 0.0014
0016638 450 2bbkL
[36] 48 Involved in protein-protein interactions 0.002
0042030 395 1n7lA
[32] 13 Important loop connects two helices 0.002
0016566 382 1dvoA
[31] 148 Part of large negatively charged region for RNA binding 0.003
0004016 168 1azsA
[34] 501 Part of binding pocket of FKP
‡
0.006
0004879 149 1ie9A
[37] 288 Forms part of active site pocket 0.007
0016813 137 1wohA
[30] 272 One of the active site residues is present 0.007
0016247 107 1oaw
[38] 30 Conserved cysteines are present 0.009
0004930 98 1ijyA
[29] 113 Surface exposed loop with conserved 'WP' sequence 0.01
0004383 92 1xbnA
[39] 74 Forms part of HEM binding pocket 0.01
0005158 61 1qqgB
[40] 56 Part of a cationic cluster
§
0.02
0008428 61 1b2uD
[41] 39 Interact with the active site residues 0.02
0003724 26 1fukA

[42] 341 Conserved interaction with DEAD box motif 0.04
*These proteins do not have a PROSITE sequence signature. The chain identifier is given after the four letter PDB code, wherever present.
†
Residue
number as given in PDB.
‡
Only PROSITE domain signature exists: 391-518.
§
Only PROSITE domain signature exists: 12-114.
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Representative examplesm different groups of predictions obtained from our clustering method (see Table 4 for more details)Figure 4
Representative examples from different groups of predictions obtained from our clustering method (see Table 4 for more details). The areas highlighted by
gray shading in the left panels are depicted in detail in the right panels. All functionally important regions of the proteins that were identified by our method
are shown in magenta with active site/substrate-binding residues in stick representation. Group 1: diagram from PDB entry 1df9
[43], a protease
representing examples of fragments for which no PROSITE sequence patterns are available. The residues Pro132 and Gly133 make non-polar interactions
with the residues of the NS3 protease (blue) inhibitor (cyan) at P2', while Ser135 and Ser163 make hydrogen bonds to side-chains of Ser21 at P1' and
Lys20 at P1, respectively, of the inhibitor. Group 2: diagram from PDB entry 1e7u
[44], representing examples for which PROSITE patterns are available
but do not overlap with the fragments. The identified functionally relevant region is spatially contiguous to the PROSITE predicted residues; the critical
Tyr867 residue implicated in ligand binding is highlighted as a stick model. Groups 3 and 4: diagram from PDB entry 1tgj
[45], representing examples where
PROSITE pattern overlaps with the fragment. The fragment derived sequence pattern overlaps with the amino-terminal part of the PROSITE pattern
(PS00250), which is annotated as a cytokine involved in the repair of tissues. Trp30 and Trp32 interact with the bound dioxane.
Ser163
Lys20
Pro132
Ser135
Ser21

Gly133
Tyr867
Trp32
Dio
Trp30
Group 1
Group 2
Groups 3 and 4
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Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.8
extend beyond the same secondary structure segment (or vice
versa). This causes the two fragments to have drastically dif-
ferent cluster populations in the final output, although they
span the same protein segment, resulting in significantly dif-
ferent GO propensities. It appears that propensity values
from longer FLs in such cases should be cautiously
interpreted to make a combined evaluation. These observa-
tions indicate that the best assessment of functional relevance
of the fragments through GO-based propensity is dependent
on both the optimal length of the fragment chosen for cluster-
ing as well as the level of the GO MF used for the calculation.
A systematic study to delineate these issues is underway.
Features of high propensity (≥20) fragments
There are 4,400 (from 526 PDB entries) 8-mers with propen-
sity ≥20. For these fragments, since we know that a majority
are directly related to protein biochemical function, we
sought to ask if they had any unique features in terms of dis-
tribution of secondary structure, hydrogen bonding, surface
accessibility and hydrophobic content preferences (Figure 6,
insets). The overall distribution of secondary structures and

hydrophobicity properties was found to be similar with
respect to the distribution observed for the entire clustered
data set (Figure 6, main plots). Substantial differences were
noticed for the hydrogen bonding pattern and relative side-
chain accessibility. A considerable number of functional frag-
ments are stabilized by inter-fragment hydrogen bonds and
more than 50% of them have a relative side-chain surface
accessibility of greater than 30. This may be due to the fact
that functional residues are positioned strategically and often
they are surface exposed. Below we describe cluster proper-
ties in more detail.
Secondary structure content
The percentages of secondary structures (H = helical, B =
beta, T = loop, C = irregular structure) of residues in all func-
tionally important FL8 fragments (propensity ≥20) identified
in this work are plotted in the inserts of Figure 6a-d. The same
plot was drawn taking average secondary structure content in
a cluster. We found that the distributions of the secondary
structures in both sets are approximately similar; only for
turns is the peak in the 0-10% content range increased four-
fold compared to the corresponding peak for all FL8 clusters.
Looking at the general features of the clusters, we find that
the FL8 clusters have lower helical content than FL16 clus-
ters. The fraction of clusters having minimal (0-10%) helical
content decreases more than half from 43% to 17% for FL8
and FL16, respectively. The trend is reversed for β-strands,
where it is known that the mean length is between five and six
residues [46]. The content of both turns and irregular second-
ary structure in clusters is significantly restricted between 0%
and 30%. More importantly, these distributions are similar to

those from randomly shuffled pseudo-clusters, suggesting
that turns and coils have a minor role in cluster formation
based on conformation. There are only a few turn and coil
dominated functional fragments. It may be noted that the dis-
tribution of helical and β secondary structures from randomly
shuffled pseudo-clusters is more narrow in contrast to
observed clusters, suggesting that precise combinations of
secondary structural elements are essential for formation of
structural motifs. This is consistent with the fact that permu-
tations of secondary structural elements result in divergence
and new topologies [47].
Hydrogen bonding
We calculated the ratio of intra-fragment hydrogen bonds to
all the hydrogen-bonding contacts made by the individual
fragment. Looking at the distribution of intra-fragment
hydrogen bonding in functionally important fragments (Fig-
ure 6e, inset) suggests that availability of unsatisfied
hydrogen bonding potential of fragments is important for
function, as manifested by low occurrence of intra-fragment
hydrogen bonds (higher peak in 0-5 range). Looking at the
average fraction of intra-fragment hydrogen bonds in
clusters, the number of clusters with no intra-molecular
hydrogen bonds is highest for FL8; the trend is reversed for
FL16, where helical content is significantly higher (Figure 6a).
As can be seen, the major peak for FL8 at 20% is shifted to
Table 4
Details of representative functionally important fragments of FL8 enumerated using GO level 3
PDB (group number)* GO MF (EC number) PROSITE pattern Molecular function Functionally important fragment(s)
(start: sequence (propensity))
†

P-value
1df9A (1) 0003724 (3.4.21.91) - Dengue virus NS3 protease 132: PGTSGSPI (30) 4.17e-5
133: GTSGSPII (40) 5.95e-8
156: TRSGAYVS (24) 0.007
1e7uA
(2) 0016773 (2.7.1.153) PS00915 Phosphatidyl-inositol 3- and
4-kinase signatures 1 and 2
857: TESLDLCL (48) 0.02
PS00916 861: DLCLLPYG (23) 0.04
872: TGDKIGMI (29) 0.03
1tgj
(3/4) 0005160 PS00250
‡
Cytokines (repair of tissue) 27: DLGWKWVH (305) 0.04
*The chain identifier is given after the four letter PDB code, wherever present.
†
Amino acids in bold either directly or indirectly participate in the
enzyme function.
‡
PROSITE pattern: (33-48, VHEPKGYYANFCSGPC).
Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.9
Genome Biology 2008, 9:R52
Mapping of high prensor 1woh
[30], shown on a backdrop of the multiple alignment of ureohydrolase superfamily enzymesFigure 5
Mapping of high propensity fragments for PDB entry 1woh
[30], shown on a backdrop of the multiple alignment of ureohydrolase superfamily enzymes.
The start positions of high propensity fragments are marked by triangles in the last six rows of each panel. Binned propensity values are given in the color
legend. Prop8, propensities derived from FL8, GO level 3 mapped from GO:0016813; Prop8_1, propensities derived from FL8, GO level 4 mapped from
GO:0016813; Prop8_2, propensities derived from FL8, GO level 4 mapped from GO: GO:0046872; Prop16, Prop16_1, and Prop16_2 refer to the same
information, except that it was derived from FL16. The residue numbers are indicated for 1woh

, which is DR agmatinase: Agm_Dra (SWISS-PROT entry
Q9RZ04). Other proteins in the alignment are Agm_Eco for agmatinase from E. coli (P60651); Agm_hum for agmatinase from human mitochondria
(Q9BSE5, residues 1-35 deleted); Arg_rat for arginase I from rat liver (P07824); Arg_Bca for arginase from Bacillus caldovelox (P53608); and PAH_Scl for
proclavaminate amidinohydrolase from Streptomyces clavuligerus (P37819). Secondary structure elements are shown as cylinders for helices and fat arrows
for β-strands. Strictly conserved residues and semi-conserved residues are colored red and yellow, respectively. Above the sequences, blue circles indicate
the residues that coordinate Mn
2+
ions. In the same panel as residue numbers, brick-red colored inverted triangles indicate residues putatively interacting
with the guanidinium group of agmatine. Green inverted triangles indicate the residues observed in the crystal structure to be interacting with the bound
inhibitor. Further details may be obtained from [30]. The figure was drawn using the program ALSCRIPT [69].
20-40
41-70
71-100
101-130
131-160
161-190
191-220
221-250
251-280
281-331
Genome Biology 2008, 9:R52
Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.10
The distribution of secostural content in observed and pseudo-clusters of FL8 and FL16Figure 6
The distribution of secondary structural content in observed and pseudo-clusters of FL8 and FL16. The statistical significance of the observed distribution
can be estimated by comparing the respective plots for the pseudo-clusters. (a) helical; (b) β-strand; (c) turn; (d) irregular secondary structure. (e,f)
Plots of normalized frequency of average percent of intra-hydrogen bonds (e), and percent relative side chain accessibility (f). The x- and y-axes of insets
are the same as in the main figures, and depict information from the functionally important fragments with propensity ≥20 identified in this work.
10 20 30 40 50 60 70 80 90 100
0
20

40
60
80
100
(a)
Normalized frequency
Average % of helical content
FL8
Random_FL8
FL16
Random_FL16
10 20 30 40 50 60 70 80 90 100
0
20
40
60
80
100
(b)
Average % of beta strand content
10 20 30 40 50 60 70 80 90 100
0
20
40
60
80
100
(c)
Normalized frequency
Average % of turn content

10 20 30 40 50 60 70 80 90 100
0
20
40
60
80
100
(d)
Average % of coil content
0 102030405060708090100
0
20
40
0 102030405060708090100
0
10
20
30
40
50
0 102030405060708090100
0
5
10
15
20
25
0102030405060708090100
0
10

20
30
40
50
0 5 10 15 20 25 30 35 40 45 50
0
20
40
60
80
100
(e)
Normalized frequency
Average % intra hydrogen bond
15 20 25 30 35 40 45 50
0
20
40
60
80
100
(f)
Average % relative side-chain accessibility
0 102030405060708090
0
2
4
6
8
10

12
14
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70
0
5
10
15
20
25
30
Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.11
Genome Biology 2008, 9:R52
25% in FL16 in pseudo-clusters; this suggests that among
other intermolecular interactions, the ubiquitous presence of
hydrogen bonding is the major driving force for large or
supersecondary structural motif formations in proteins.
Relative side-chain accessibility
Functional residues have a distinct preference for either full
burial or high solvent exposure; as a result the plot for the
solvent exposure (Figure 6f) has two peaks, one at 0-25 Å
2
and another at 30-70 Å
2
. This is in contrast to the unimodal
distribution of average solvent exposure of clusters centered
at 30-40 Å
2
for both FL8 and FL16. The same calculations
using pseudo-clusters show a peak at a greater burial than the
mean of the FL8 and FL16 observed distribution, suggesting

that structural motifs do prefer more exposed locations in the
tertiary structure, in contrast to both buried and exposed
functional motifs.
Hydrophobic content
All fragments, including functionally important ones, show a
non-preferential hydrophobicity distribution. We calculated
hydrophobicities of functionally important fragments and the
average hydrophobicites of clusters using Wolfenden [48]
and Kyte-Doolittle [49] scales. The graphs show normal dis-
tributions for both the scales, as well as with calculations
using pseudo-clusters; all graphs for a given scale share the
major peak around the same bin (data not shown).
Conformational diversity of identical sequences and
implications for protein function
The presence of identical peptide fragments in multiple clus-
ters offers lessons for protein engineering, design and func-
tional requirement/perturbation arising from
conformational promiscuity. It has been previously shown
that identical peptides can have completely different confor-
mations in unrelated proteins [50,51]. We revisited the previ-
ous observation by analyzing our clustering results, including
the data set from FL5. The clustering of penta-peptide frag-
ments showed nearly 10.4% (0.16% for the FL8 data set) of
the fragments in the clusters (47,227 out of 455,305) to have
at least two different conformations (Table 5). Further, the
nature of structural transition between the conformations
was analyzed using secondary structure definition according
to the DSSP algorithm [52]. Only four different secondary
structural states (H, B, T and C) were considered for a residue
in a fragment. For each identical sequence found in more than

one cluster, the conformational state at each position of the
fragment was matched/compared to identify the structural
transition between them. It is noteworthy that 42% of the FL5
repeat sequences have no match in all of the five-positions,
implying they are totally dissimilar conformations (Table 5).
When the analysis was repeated using FL8 fragments, the
fraction decreased to 4.6%, while at FL16, no identical frag-
ments were found across multiple clusters. Looking at identi-
cal sequences found across multiple clusters, 10.2% of the
FL5 sequences are found across 2 clusters; whereas only 1.5%
of sequences are found across 3 or more clusters. The
sequence SGPSS, an all trans peptide, was found across a
maximum of 32 clusters. Interestingly, when an identical
sequence is found across more clusters, the difference in
secondary structure tends to become less; as a result, there
are only limited variations in the actual three-dimensional
conformation of the fragments.
We also checked which sequentially identical FL8 fragments
present across multiple clusters had a high propensity. We
found 235 (some of them overlapping) fragments from 57 dif-
ferent PDB files with propensity ≥5 and p-value ≤0.05. Of
these, only 93 sequences from 31 PDB files had propensity
≥20.0. We randomly selected a few of these to assess how
these conformationally promiscuous fragments were func-
tionally relevant to the protein activity (Table 6). We found
Table 5
Statistics on identical sequences occurring across clusters
Number of times found across the clusters Number of sequences (percentage) Number of matches between
the conformational states
Number of cases (percentage)

FL5 FL8 FL5 FL8
1 41,716 (88.3) 693 (98.4) 0 22,875 (41.8) 33 (4.6)
2 4,819 (10.2) 10 (1.4) 1 8,181 (15.0) 42 (5.9)
3 528 (1.1) 1 (0.2) 2 7,104 (13.0) 54 (7.5)
4 69 (0.2) 3 6,484 (11.8) 72 (10.1)
5-32 11-1 (0.2) 4 5,505 (10.1) 77 (10.8)
5 4,542 (8.3) 94 (13.1)
6 128 (17.9)
7 101 (14.1)
8 115 (16.1)
Genome Biology 2008, 9:R52
Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.12
five sequences from the amino-terminal extracellular domain
intradiskal loop of rhodopsin (PDB: 1u19A
[53], 1edsA [54],
1edxA
[54], 1edvA [54]) potentially involved in G-coupled
signaling activity; the importance of conformational transi-
tion in G-coupled signal transduction is fairly well studied. In
the eukaryotic translation initiation factor (PDB: 1kl9
[55]),
the intra- and inter-domain movements are critical for tRNA
binding during translation. Interestingly, our method
revealed a fragment from human transforming growth factor
β3 (PDB: 1tgj
[45]) containing cysteine residues that were
found to destabilize the protein when the disulfide bond was
reduced. This hints at the important role of the fragment in
conformational stability of structure and function. In PDB
entry 1q9b

[56], a IgE-binding natural allergen, the predicted
fragments spanning residue positions 6-22 form the part of
the conformational epitope experimentally observed to
impart binding activity through Trp. In the P-type ATPase
family, Ca
+2
-ATPase of the skeletal muscle sarcoplasmic
reticulum contains a flexible fragment experimentally corrob-
orated and also found in this study (PDB: 1wpgA
[57]). This
fragment spanning residues 349-357 contains an Asp at posi-
tion 351 that is phosphorylated, triggering this conforma-
tional transition. A similar example from Neurospora plasma
membrane H
+
ATPase, spanning fragment 377-384 found in
this study, contains an Asp at position 378 that is reversibly
phosphorylated, which triggers a conformational change in
the protein, allowing it to function as a proton pump (PDB:
1mhsA
[58]). Interestingly, additional conformationally
flexible fragments spanning 631-640 revealed by this study lie
in a spatially contiguous location to fragment 377-384, indi-
cating the requirement of conformational flexibility of not
only the fragment triggering the transition, but also the
neighboring segments. These results highlight how our pro-
pensity-based method is able to screen for functionally
important fragments, selecting protein segments influencing
dynamic structure and plasticity.
Discussion

Clustering peptide fragments has been long practiced by
structural biologists as a means to understand protein fea-
tures; however, our method of assessing fragment-function
links using GO has not been done before. The existing
approaches of function assessment mostly use information at
Table 6
Identical sequences of FL8 present across multiple clusters with GO MF propensity calculated using level 3*
PDB [reference]
†
Molecule Putative fragment function Sequence (propensity)§ P-value
1u19A
‡
[53] Rhodopsin Part of extracellular domain intradiskal loop
involved in cell signaling
11: VPFSNKTG (47) 0.02
1edsA
[54] Bovine rhodopsin Same as above 17: GCNLEGFF (93) 0.01
21: EGFFATLG (39) 0.03
22: GFFATLGG (130) 0.008
1edvA
[54] Bovine rhodopsin Same as above 16: CGIDYYTPP (96) 0.01
1edxA
[54] Bovine rhodopsin Same as above 11: VPFSNKTG (22) 0.04
1tgj
‡
[45] Human transforming growth factor β3 Structure destabilized on dislufide bond reduction 72: ASASPCCV (157) 0.006
1kl9
A
‡
[55] Human translation initiation factor 2α Linker for the penultimate 3

10
helix and the last α-
helix in domain 1
163: DSLDLNED (35) 0.03
164: SLDLNEDE (35) 0.003
1q9b
A
‡
[56] Hevein (IgE bonding natural allergen) Part of conformational epitope 6: QAGGKLCP (62) 1.3e-08
8: GGKLC
PNN (299) 2.3e-08
9: GGLC
PNNL (123) 9.8e-12
11: LC
PNNLCC (25) 1.3e-06
12: C
PNNLCCS (28) 2.0e-08
14: NNLCC
SQW (28) ≈ 0
15: NLCC
SQWG (79) 1.5e-08
1wpg
A
‡
[57] Sarcoplasmic/endoplasmic reticulum
calcium ATPase
Phosphorylation of D351 causes the protein to
switch conformation
349: CSDKTGTL (41) 0.002
350: SDKTGTLT (56) 0.001

1mhs
A
‡
[58] Proton ATPase Phosphorylation of D378 causes the protein to
switch conformation
631: MTGDGVND (22) 0.008
633: GDGVNDAP (25) 0.04
376: CSDKTGTL (41) 0.002
377: SDKTGTLT (56) 0.001
*The highest propensity fragment from only one cluster is shown.
†
Files indicated in regular font denote an NMR-derived structure.
‡
An X-ray-
derived structure. The chain identifier is indicated after the four letter PDB code, wherever present.
§
Disulfide bonded Cys are underlined.
Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.13
Genome Biology 2008, 9:R52
some level from either annotated sequence or structure infor-
mation for prediction/mapping of the functional regions in
protein structures (for example, Espadaler et al. [21]). In con-
trast, our method does not use prior knowledge on fragments;
most importantly, only GO terms and a group of geometri-
cally similar fragments are considered for dissecting the func-
tional regions. The procedure we follow consists of three
steps. In the first step we cluster the fragments based solely
on geometric considerations using backbone torsion angles.
This identifies a conformationally similar set of peptides. It is
important to note that at this stage of the grouping, fragments

from all parts of the protein structure, not solely those
restricted to loops and turns, are taken into account. In the
second step, we assign molecular functions to the fragments
in a given cluster from level-specific mapping of molecular
function terms using the GO graph. In the third step, we iden-
tify statistically significant benchmarks for protein fragments
that are reliably associated with MF. This novel composite
procedure has helped in delineating new protein fragments
associated with function. Another attractive feature of our
method is that we characterize functions of fragments at dif-
ferent levels of the GO, which allows for continual improve-
ment as the GO database grows.
The method of agglomerative clustering as implemented is
also new as applied to the protein fragments. Our method is
unique because of the self-organizing ability of the cluster
centers; this allows the clusters to be centered on the densest
distribution of points in the torsion space. Moreover, we use
two distance measures to group the fragments: the first is the
Euclidian distance between the φ,ψ torsion angles of the
fragment and the cluster center, and the second is the pair dif-
ference between torsion angles at equivalent positions of the
fragment under consideration and the cluster center. While
the former gives a global measure of similarity, the latter indi-
cates the local similarity. The two distances in combination
give a conformationally homogenous distribution of
fragments in the cluster in a way that facilitates their dissec-
tion according to functional importance.
It is not our claim that our method is computationally supe-
rior to or computationally more efficient than other methods
assessing function. We would like to emphasize that ours is an

entirely new method that enables discovery of new sets of
fragments associated with function in a statistically rigorous
fashion. It can be alluded to as a protein-fragment-geometry
derived assessment method, where instead of using primary
sequence information to derive function from canonical
sequence-structure-function relationships, we have used geo-
metric alignment and the GO to dissect important fragments
linked to function. While structural comparison works well at
the level of protein fold, at smaller structural sizes many
diverse sequences may have similar conformations, making
difficult the decomposition of fragment functional properties
in a quantitative way. Our propensity calculations are able to
filter a subset of fragments that may indeed be linked to the
protein function. P-values calculated using the
hypergeometric distribution lend credence to the results in a
statistically rigorous fashion.
The utility of the method to the biologist is multifarious. For
example, once a fragment has been identified that can be
linked to function, this information is useful for assessing
putative functions of new proteins, as well as guiding protein
engineering experiments or designs with desired functionali-
ties. Our example of PDB entry 1woh
[30] shows how frag-
ments proposed from our method map on to functionally
important and sequentially conserved regions of the
molecule. It also raises an important question as to whether
our method can predict important fragments for all proteins,
since every protein has a function. In principle, this is possi-
ble as we can extend the coverage of our method by varying
the clustering parameters, and make it more selective by sub-

clustering to better assess the ranking/importance of frag-
ments vis-à-vis their direct relevance to MF. A fragment
library created from such high propensity fragments can be
used in annotating proteins with unknown function. In these
cases the calculations are preferably done at a deeper level of
5 or more in the GO directed acyclic graph, and appropriate
propensity value thresholds should be used for screening the
fragments after plotting the propensity distribution.
Proteins containing high-propensity fragments as identified
by our methodology appear to be ideal candidates for protein
engineering and design experiments, as they provide func-
tionally important sites that can be targeted for inhibition. As
can be seen, the ranges of functions in which the fragments
are important include both enzymatic and non-enzymatic
functions. For example, in PDB entry 1df9
[43], which is a
Dengue virus protease that processes polyproteins, residues
that interact with the substrate (Asp129, Tyr150 and Ser163)
are absolutely conserved among almost all of more than 70
flaviviruses. But our conformational analysis suggests that
fragments spanning residues 132-140, and 156-163 are also
very important in providing the correct receptor site for the
substrate. Therefore, mutation in these regions would also
modulate the turnover of the protease as well as its specificity
for substrate.
While making decisions on protein design one can make use-
ful inferences from our clustering results based on variation
of structural stability with peptide lengths. Similarly,
sequences that are conformationally promiscuous can be
easily recognized and included/excluded during design as

needed. Coupling protein fragments with function using pro-
pensity also provides a useful opportunity for understanding
the amyloidogenic propensity of peptides [59] and drug tar-
gets, especially in 'conformational diseases'.
Although secondary to the main objectives of this work, the
clustering results obtained are of direct interest in under-
standing the inverse protein-folding problem. Of the FL8
Genome Biology 2008, 9:R52
Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.14
fragments, 92% have a partner with similar conformation.
This suggests that efficient assembly of protein folds based on
fragments is realistically possible. Two important observa-
tions available from Figure 6 are the role of hydrogen bonds
in accommodating a given conformation, and the importance
of the order of secondary structures in the polypeptide chain,
rather than the overall hydrophobicity in accommodating
diverse sequences into a specific fold. It may be noted that the
data set we have chosen is highly unbiased, because each pro-
tein in the data set is a distinct fold. The amino acid identity
between proteins is therefore expected to be below 20%.
Therefore, our data reflect which unbiased properties may be
essential in making diverse sequences compatible to a given
fold. Further property-based sub-clustering will be useful in
these regards for development of ab initio methods of protein
modeling.
Conclusion
Our proposed clustering-cum-function analysis method is
useful in dissecting/identifying protein fragments based on
their relevance to function. Its application to propensity-
based functional inference on identical fragments across

multiple clusters highlights its diverse utility. In particular,
the absence of any sequence alignment step in the method
makes it a valuable tool to predict functionally important
regions in hypothetical proteins from structural genomics
projects. The data provided by the method comprise a nucleus
on which our future sequence-cum-geometric signature pat-
tern libraries will be developed. It will benefit function anno-
tation efforts, as well as protein engineering, design and
modeling studies.
Materials and methods
PDB files
The list of PDB files for clustering was obtained from the
DALI Domain Dictionary [60] by choosing one representative
PDB entry per fold (Additional data file 1). The PDB file with
best resolution and R-factor was chosen.
Secondary structure representation
The backbone torsion angles of each PDB file were assigned
using the program SECSTR of the PROCHECK suite [61]. The
secondary structure of each residue was classified into four
states, helical (H), β-strand (B), loop (T) and irregular struc-
tures (C) for each residue in a fragment. Symbols H/h, G/g,
and P/p denoting α-helix, 3
10
-helix, and π-helix, respectively,
were merged and treated as H; E/e and B, denoting β-strand
and β-ladder, respectively, were merged and treated as B; T/t
and S/s, denoting turn and geometrical bends, respectively,
were merged and treated as T; blank, denoting irregular sec-
ondary structure, were treated as C.
Clustering procedure

To cluster the fragments from a protein structure, the back-
bone is divided serially into overlapping fragments with spec-
ified FL and torsion (φ,ψ) angles for the fragment residues
and put into an array. Because the terminal residues (or
where there is a chain break) of the protein do not have φ/ψ
angles, these residues are not included in the fragment. Also,
residues with main-chain atoms with a B-factor >60 Å
2
are
rejected. This ensures that in the absence of a threshold reso-
lution and R-factor for selecting structures modeled from
electron densities, we chose fragments that did not incorpo-
rate large coordinate errors. For NMR derived structures, we
always chose the first model in the PDB file. The omega angles
were checked to ensure all the peptide bonds are trans in the
fragment. Any fragment with a cis peptide bond was ignored
for our current analysis. A peptide bond is considered to be a
cis bond if the absolute value of the omega angles are less than
or equal to 90°. For a fragment length of 8, eight pairs of dihe-
dral angles will be used for clustering (FL = 8).
For each protein of length n to be included in the search, we
first compute the following series of dihedral angles: {(
φ
,
ψ
)
1
(
φ
,

ψ
)
2
(
φ
,
ψ
)
3
(
φ
,
ψ
)
4
(
φ
,
ψ
)
5
(
φ
,
ψ
)
6
(
φ
,

ψ
)
7
(
φ
,
ψ
)
8
(
φ
,
ψ
)
9
(
φ
,
ψ
)
10
(
φ
,
ψ
)
11
(
φ
,

ψ
)
12
(
φ
,
ψ
)
n-1
(
φ
,
ψ
)
n
}, where n is the number of
amino acids used to obtain the fragments from a protein
structure. The peptide chain is then decomposed into a series
of overlapping fragments of specified length (FL = 8, for
example, as depicted below):
F
1
: [(
φ
,
ψ
)
2
(
φ

,
ψ
)
3
(
φ
,
ψ
)
4
(
φ
,
ψ
)
5
(
φ
,
ψ
)
6
(
φ
,
ψ
)
7
(
φ

,
ψ
)
8
(
φ
,
ψ
)
9
]
F
2
: [(
φ
,
ψ
)
3
(
φ
,
ψ
)
4
(
φ
,
ψ
)

5
(
φ
,
ψ
)
6
(
φ
,
ψ
)
7
(
φ
,
ψ
)
8
(
φ
,
ψ
)
9
(
φ
,
ψ
)

10
]
F
n-7
: [(
φ
,
ψ
)
n-8
(
φ
,
ψ
)
n-7
(
φ
,
ψ
)
n-6
(
φ
,
ψ
)
n-5
(
φ

,
ψ
)
n-4
(
φ
,
ψ
)
n-3
(
φ
,
ψ
)
n-2
(
φ
,
ψ
)
n-1
]
We define the distance between two fragments [F
i
, F
j
] as:
where l, m are the starting positions of the fragments [Fi, Fj],
respectively.

For every (
ψ
im
-
ψ
jm
), if |
ψ
im
-
ψ
jm
| > 180,
then use 360 - |
ψ
im
-
ψ
jm
|
For every (
φ
im
,
φ
jm
)) if |
φ
im
-

φ
jm
| > 180,
then use 360 - |
φ
im
-
φ
jm
|
Assume a set of similar fragments forms a group and L is the
index label that identifies the groups. We define the center of
group L, C
L
, as [(
φ
j1
,
ψ
j1
), (
φ
j2
,
ψ
j2
), (
φ
j8
,

ψ
j8
)], where:
DIST
Fi Fj ix jy
xlym
lm
ix jy
xlym
l
[,]
,
,
,
() ( )=−+−
==
++
==
+
∑
φφ ψψ
2
77
2
7,,
/
m+
∑
⎡
⎣

⎢
⎢
⎢
⎤
⎦
⎥
⎥
⎥
7
12
Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.15
Genome Biology 2008, 9:R52
where N
L
is the number of fragments F in the group, and the
sum is over i. The cyclic nature of the (
φ
,
ψ
) values has been
preserved by adding -360° if any
φ
/
ψ
is >180° or by adding
360° if any
φ
/
ψ
is <-180°. The distance between fragment F

i
and the center of group L, C
L
is given as DIST
[Fi, CL]
.
Algorithm
Input: a set of
φ
,
ψ
from F
Output: a set of groups into which the points have been
divided, where every point in a group is within the distance
(DIST) threshold R from its group center C
L
and angle differ-
ence at each position in the fragment and group center C
L
does not exceed ANG.
Begin
I. Pick an arbitrary fragment (it is the seed fragment and
starting cluster center C
1
)
Until the last remaining fragment do
{
Find distances between C
L
(L = 1, L

max
) and the frag-
ment F
k
.
L
max
= maximum number of cluster centers existing at
that point of time.
φ
iCL
-
φ
iFK
=
φ
angle difference at position i in cluster
center L and fragment K.
ψ
iCL
-
ψ
iFK
=
ψ
angle difference at position i in cluster
center L and fragment K.
If DIST
[CL, Fk]
≤ R and (

φ
iCL
-
φ
jFK
) ≤ ANG and (
ψ
iCL
-
ψ
jFK
) ≤ ANG{
Insert F
k
into group L and add 1 to N
L
Compute the new center C
L
' of group L
} Else {make the fragment a new cluster center
C
L+1
}
}
II. For each fragment in the list {
a). Find distances between C
L
(L = 1, L
max
) and the

fragment F
k
.
If DIST
[CL, Fk]
> R or (
φ
iCL
-
φ
jFK
) > ANG or (
ψ
iCL
-
ψ
jFK
) > ANG {
1. Reject F
k
from group L and subtract 1 from N
L
2. Compute the new center C
L
' of group L
3. Do a). for fragment F
k
.
If DIST
[CL, Fk]

≤ R and (
φ
iCL
-
φ
jFK
) ≤ ANG and
(
ψ
iCL
-
ψ
jFK
) ≤ ANG
{
Insert F
k
into group L and add 1 to N
L
Compute the new center C
L
' of group L
} Else {make the fragment a new cluster
center C
L+1
}
}
b). Keep count of number of fragments rejected
}
If number of fragments rejected in previous round > cur-

rent round do { II }
else { print cluster details}
END
For our clustering runs, we used R = 30° × X, where X is the
fragment length and ANG = 60°. The code has been imple-
mented in PERL and is available from the authors upon
request.
Generation of pseudo-clusters
Clusters are built by randomly picking fragments from the
total fragment library of a given length. The total number of
fragments in each set of pseudo-clusters added up to 100,000
fragments. The distribution of physicochemical properties of
clusters was averaged over 30 generated sets in order to gen-
erate base values for the estimate of statistical significance.
Identification of functionally important fragments
The GO term, which corresponds to the MF of the protein in
the PDB, was taken from the GOA annotation [62]. Accord-
ingly, each fragment in the cluster was assigned to a GO MF
term of its PDB entry. The parent functions for each fragment
MF term at a given level from the root node were identified
φφψψ
jm im L
i
N
jm im L
i
N
NN
LL
== =

==
∑∑
() ( )
/ ; / , ( , , )
11
12 8m
Genome Biology 2008, 9:R52
Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.16
from the GO directed acyclic graph (Figure 2). We have car-
ried out the analysis at levels 3, 4, and 5 (level 3 implies that
the parent is at three edges from the root node GO:0003674).
The propensity was calculated for each fragment function in a
cluster using the following formula:
where n
X
and N
X
are the number of GO MF term 'X' in a clus-
ter and in all clusters, respectively, and n
T
and N
T
stand for the
number of all functions in that particular cluster and in all
clusters, respectively. L stands for the GO level at which the
MF was mapped for the calculations. CATH identifier based
propensity calculations were done the same way by replacing
the GO term, wherever the CATH identifier for a protein was
available. P-values for individual GO terms were calculated
using the hypergeometric distribution formula as follows:

where symbols are the same as in the propensity equation.
The probability of a GO term X among K GO terms in a cluster
is given by , and applying the Bonferroni correc-
tion, the p-value of the GO term X occurring k times in the
cluster is . A canonical threshold of ≤0.05
was used to identify the statistically significant fragments
using the said formula.
For the structure-sequence pattern analysis, each sequence of
all the fragments with propensity ≥20 was searched with the
program BLAST [63] using short and nearly exact match
against the UNIPROT database [64] of sequences. The hits
with at least one PDB entry were taken for further PROSITE
pattern searches. The full sequences of such fragments with
one PDB hit were scanned for PROSITE sequence signature
patterns and subsequently classified into different groups
(see Results for details). The selection scheme was used to fil-
ter down the number of possible hits to be manually reviewed
from the literature, and also test if the fragments alone are
able to pick out homologous PDB sequences, which could be
further used for detailed investigations as needed.
Information content
The information content of the fragments was obtained using
the Shannon entropy measure formula [65]. For a given posi-
tion in the fragment, the entropy was calculated as:
S (at a given position) = -∑w log(w)
where the summation runs over all amino acids and w stands
for the fraction of occurrence of each residue at that position.
An average of entropies at each position was taken to calcu-
late the average information content of the cluster. A value S
= 0 means that the position is fully conserved and a more pos-

itive S implies the position is diverse in amino acids.
Surface accessibility
The percent relative side-chain accessibility of the fragments
in a cluster was calculated using the program NACCESS [66]
with a probe radius of 1.4 Å. A standard Ala-X-Ala tripeptide
in extended conformation was used for calculation of percent
relative accessibility.
Hydrogen bonds
Hydrogen bonds were calculated using HBPLUS [67] with
hydrogen bonding parameters (D-A distance ≥ 3.9 Å, H A ≥
2.7 Å, D-H A ≥ 90°).
Abbreviations
B, beta; C, irregular structure; FL, fragment length; GO, Gene
Ontology; H, helical; MF, molecular function; PDB, Protein
Data Bank; T, loop; TGF, transforming growth factor.
Authors' contributions
KM wrote programs, carried out analysis and provided help
with the literature review and drafting of the manuscript. DP
designed and conceived the study, wrote programs, per-
formed analysis and drafted the manuscript. SR participated
in conceiving the study, provided input into the design of the
study and helped in reviewing the manuscript drafts. NEB,
SSI, and GS participated in mathematical formulation of the
clustering algorithm. All authors read and approved the final
manuscript.
Additional data files
The following additional data are available. Additional data
file 1 is a table listing the PDB files used in this work, culled
from the FSSP library. Additional data file 2 is a histogram
showing the distribution of compactness values for FL8 and

FL16 clusters.
Additional data file 1PDB files used in this work, culled from the FSSP libraryPDB files used in this work, culled from the FSSP library.Click here for fileAdditional data file 2Distribution of compactness values for FL8 and FL16 clustersDistribution of compactness values for FL8 and FL16 clusters.Click here for file
Acknowledgements
MK thanks CSIR (India) for a fellowship. DP thanks the Department of Bio-
technology, New Delhi (DBT), for funds under the Virtual Centre of Excel-
lence in tuberculosis research. Funding for the Bioinformatics center by
DBT is gratefully acknowledged. RS thanks International Business Machines
(IBM) for a CAS fellowship grant to his research group. This work was sup-
ported in part by DOE-ORNL grant 4000008407 and by an NSF grant. The
authors thank Pralay Mitra, Zhi Li, Sumeet Dua and Jacob Bahren for their
help, and Christopher Miller for critically reading the manuscript.
Propensity
L
XL
TL
XL
TL
n
n
N
N
=
())(
Hn N n N
n
n
Nn
Nn
N
N

LXL TLTL XL
TL
XL
TL TL
XL XL
TL
XL
(; ,, )=
⎛
⎝
⎜
⎞
⎠
⎟
−
−
⎛
⎝
⎜
⎞
⎠
⎟
⎛⎛
⎝
⎜
⎞
⎠
⎟
1
0

1
−
=
−
∑
Ht
t
K
()
kHt
t
K
×−
=
−
∑
(())1
0
1
Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.17
Genome Biology 2008, 9:R52
References
1. Friedberg I, Godzik A: Connecting the protein structure uni-
verse by using sparse recurring fragments. Structure 2005,
13:1213-1224.
2. Han KF, Baker D: Global properties of the mapping between
local amino acid sequence and local structure in proteins.
Proc Natl Acad Sci USA 1996, 93:5814-5818.
3. Kolodny R, Koehl P, Guibas L, Levitt M: Small libraries of protein
fragments model native protein structures accurately. J Mol

Biol 2002, 323:297-307.
4. Unger R, Harel D, Wherland S, Sussman JL: A 3D building blocks
approach to analyzing and predicting structure of proteins.
Proteins 1989, 5:355-373.
5. Haspel N, Tsai CJ, Wolfson H, Nussinov R: Reducing the compu-
tational complexity of protein folding via fragment folding
and assembly. Protein Sci 2003, 12:1177-1187.
6. Tsai CJ, Polverino de Laureto P, Fontana A, Nussinov R: Compari-
son of protein fragments identified by limited proteolysis
and by computational cutting of proteins. Protein Sci 2002,
11:1753-1770.
7. Jonassen I: Methods for Discovering Conserved Patterns in Protein
Sequences and Structures Oxford: Oxford University Press; 2000.
8. Grabarek Z: Structural basis for diversity of the EF-hand cal-
cium-binding proteins. J Mol Biol 2006, 359:509-525.
9. Gamsjaeger R, Liew CK, Loughlin FE, Crossley M, Mackay JP: Sticky
fingers: zinc-fingers as protein-recognition motifs. Trends Bio-
chem Sci 2007, 32:63-70.
10. Suetake T, Tsuda S, Kawabata S, Miura K, Iwanaga S, Hikichi K, Nitta
K, Kawano K: Chitin-binding proteins in invertebrates and
plants comprise a common chitin-binding structural motif. J
Biol Chem 2000, 275:17929-17932.
11. Saraste M, Sibbald PR, Wittinghofer A: The P-loop - a common
motif in ATP- and GTP-binding proteins. Trends Biochem Sci
1990, 15:430-434.
12. Tendulkar AV, Joshi AA, Sohoni MA, Wangikar PP: Clustering of
protein structural fragments reveals modular building block
approach of nature. J Mol Biol 2004, 338:611-629.
13. Ferré S, King RD: Finding motifs in protein secondary struc-
ture for use in function prediction. J Comput Biol 2006,

13:719-731.
14. Pal D, Sühnel J, Weiss MS: New principles of protein structure:
nests, eggs - and what next? Angew Chem Int Ed Engl 2002,
41:4663-4665.
15. Watson JD, Milner-White EJ: The conformations of polypeptide
chains where the main-chain parts of successive residues are
enantiomeric. Their occurrence in cation and anion-binding
regions of proteins. J Mol Biol 2002, 315:183-191.
16. Watson JD, Milner-White EJ: A novel main-chain anion-binding
site in proteins: the nest. A particular combination of phi,psi
values in successive residues gives rise to anion-binding sites
that occur commonly and are found often at functionally
important regions. J Mol Biol 2002, 315:171-182.
17. Innis CA, Anand AP, Sowdhamini R: Prediction of functional sites
in proteins using conserved functional group analysis. J Mol
Biol 2004, 337:1053-1068.
18. Jones S, Thornton JM: Searching for functional sites in protein
structures. Curr Opin Chem Biol 2004, 8:3-7.
19. Pazos F, Sternberg MJ: Automated prediction of protein func-
tion and detection of functional sites from structure. Proc Natl
Acad Sci USA 2004, 101:14754-14759.
20. Muir TW, Dawson PE, Fitzgerald MC, Kent SB: Protein signature
analysis: a practical new approach for studying structure-
activity relationships in peptides and proteins. Methods
Enzymol 1997, 289:545-564.
21. Espadaler J, Querol E, Aviles FX, Oliva B: Identification of func-
tion-associated loop motifs and application to protein func-
tion prediction. Bioinformatics 2006, 22:2237-2243.
22. Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eil-
beck K, Lewis S, Marshall B, Mungall C, Richter J, Rubin GM, Blake JA,

Bult C, Dolan M, Drabkin H, Eppig JT, Hill DP, Ni L, Ringwald M,
Balakrishnan R, Cherry JM, Christie KR, Costanzo MC, Dwight SS,
Engel S, Fisk DG, Hirschman JE, Hong EL, Nash RS, et al.: The Gene
Ontology (GO) database and informatics resource. Nucleic
Acids Res 2004, 32(Database issue):D258-D261.
23. Holm L, Sander C: The FSSP database: fold classification based
on structure-structure alignment of proteins. Nucleic Acids Res
1996, 24:206-209.
24. Cabena P, Hadjnian P, Stadler R, Verhees J, Zanasi A: Discovering Data
Mining: From Concept to Implementation New Jersey: Prentice Hall PTR;
1997.
25. Westbrook J, Feng Z, Jain S, Bhat TN, Thanki N, Ravichandran V, Gil-
liland GL, Bluhm W, Weissig H, Greer DS, Bourne PE, Berman HM:
The Protein Data Bank: unifying the archive. Nucleic Acids Res
2002, 30:245-248.
26. Sawada Y, Honda S: Structural diversity of protein segments
follows a power-law distribution. Biophys J 2006, 91:1213-1223.
27. Pearl FM, Bennett CF, Bray JE, Harrison AP, Martin N, Shepherd A,
Sillitoe I, Thornton J, Orengo CA: The CATH database: an
extended protein family resource for structural and func-
tional genomics. Nucleic Acids Res 2003, 31:452-455.
28. Hulo N, Bairoch A, Bulliard V, Cerutti L, De Castro E, Langendijk-
Genevaux PS, Pagni M, Sigrist CJ: The PROSITE database. Nucleic
Acids Res 2006, 34(Database issue):D227-D230.
29. Dann CE, Hsieh JC, Rattner A, Sharma D, Nathans J, Leahy DJ:
Insights into Wnt binding and signalling from the structures
of two Frizzled cysteine-rich domains. Nature 2001, 412:86-90.
30. Ahn HJ, Kim KH, Lee J, Ha JY, Lee HH, Kim D, Yoon HJ, Kwon AR,
Suh SW: Crystal structure of agmatinase reveals structural
conservation and inhibition mechanism of the ureohydrolase

superfamily. J Biol Chem 2004, 279:50505-50513.
31. Ghetu AF, Gubbins MJ, Frost LS, Glover JN: Crystal structure of
the bacterial conjugation repressor finO. Nat Struct Biol 2000,
7:565-569.
32. Zamoon J, Mascioni A, Thomas DD, Veglia G: NMR solution struc-
ture and topological orientation of monomeric phospholam-
ban in dodecylphosphocholine micelles.
Biophys J 2003,
85:2589-2598.
33. Qiu X, Janson CA: Structure of apo acyl carrier protein and a
proposal to engineer protein crystallization through metal
ions. Acta Crystallogr D Biol Crystallogr 2004, 60:1545-1554.
34. Tesmer JJ, Sunahara RK, Gilman AG, Sprang SR: Crystal structure
of the catalytic domains of adenylyl cyclase in a complex with
Gsalpha.GTPgammaS. Science 1997, 278:1907-1916.
35. Russo AA, Jeffrey PD, Patten AK, Massague J, Pavletich NP: Crystal
structure of the p27Kip1 cyclin-dependent-kinase inhibitor
bound to the cyclin A-Cdk2 complex. Nature 1996,
382:325-331.
36. Chen L, Doi M, Durley RC, Chistoserdov AY, Lidstrom ME, Davidson
VL, Mathews FS: Refined crystal structure of methylamine
dehydrogenase from Paracoccus denitrificans at 1.75 A
resolution. J Mol Biol 1998, 276:131-149.
37. Tocchini-Valentini G, Rochel N, Wurtz JM, Mitschler A, Moras D:
Crystal structures of the vitamin D receptor complexed to
superagonist 20-epi ligands. Proc Natl Acad Sci USA 2001,
98:5491-5496.
38. Kim JI, Konishi S, Iwai H, Kohno T, Gouda H, Shimada I, Sato K, Arata
Y: Three-dimensional solution structure of the calcium chan-
nel antagonist omega-agatoxin IVA: consensus molecular

folding of calcium channel blockers. J Mol Biol 1995,
250:659-671.
39. Nioche P, Berka V, Vipond J, Minton N, Tsai AL, Raman CS: Femto-
molar sensitivity of a NO sensor from Clostridium botulinum.
Science 2004, 306:1550-1553.
40. Dhe-Paganon S, Ottinger EA, Nolte RT, Eck MJ, Shoelson SE: Crystal
structure of the pleckstrin homology-phosphotyrosine bind-
ing (PH-PTB) targeting region of insulin receptor substrate
1. Proc Natl Acad Sci USA 1999, 96:8378-8383.
41. Vaughan CK, Buckle AM, Fersht AR: Structural response to
mutation at a protein-protein interface. J Mol Biol 1999,
286:
1487-1506.
42. Caruthers JM, Johnson ER, McKay DB: Crystal structure of yeast
initiation factor 4A, a DEAD-box RNA helicase. Proc Natl Acad
Sci USA 2000, 97:13080-13085.
43. Murthy HM, Judge K, DeLucas L, Padmanabhan R: Crystal structure
of Dengue virus NS3 protease in complex with a Bowman-
Birk inhibitor: implications for flaviviral polyprotein process-
ing and drug design. J Mol Biol 2000, 301:759-767.
44. Walker EH, Pacold ME, Perisic O, Stephens L, Hawkins PT, Wymann
MP, Williams RL: Structural determinants of phosphoinositide
3-kinase inhibition by wortmannin, LY294002, quercetin,
myricetin, and staurosporine. Mol Cell 2000, 6:909-919.
45. Mittl PR, Priestle JP, Cox DA, McMaster G, Cerletti N, Grütter MG:
The crystal structure of TGF-beta 3 and comparison to TGF-
beta 2: implications for receptor binding. Protein Sci 1996,
5:1261-1271.
Genome Biology 2008, 9:R52
Genome Biology 2008, Volume 9, Issue 3, Article R52 Manikandan et al. R52.18

46. Penel S, Morrison RG, Dobson PD, Mortishire-Smith RJ, Doig AJ:
Length preferences and periodicity in beta-strands. Antipar-
allel edge beta-sheets are more likely to finish in non-hydro-
gen bonded rings. Protein Eng 2003, 16:957-961.
47. Vogel C, Morea V: Duplication, divergence and formation of
novel protein topologies. Bioessays 2006, 28:973-978.
48. Wolfenden R, Andersson L, Cullis PM, Southgate CC: Affinities of
amino acid side chains for solvent water. Biochemistry 1981,
20:849-855.
49. Kyte J, Doolittle RF: A simple method for displaying the hydro-
pathic character of a protein. J Mol Biol 1982, 157:105-132.
50. Kabsch W, Sander C: On the use of sequence homologies to
predict protein structure: identical pentapeptides can have
completely different conformations. Proc Natl Acad Sci USA
1984, 81:1075-1078.
51. Sudarsanam S: Structural diversity of sequentially identical
subsequences of proteins: identical octapeptides can have
different conformations. Proteins 1998, 30:228-231.
52. Kabsch W, Sander C: Dictionary of protein secondary struc-
ture: pattern recognition of hydrogen-bonded and geometri-
cal features. Biopolymers 1983, 22:2577-2637.
53. Okada T, Sugihara M, Bondar AN, Elstner M, Entel P, Buss V: The
retinal conformation and its environment in rhodopsin in
light of a new 2.2 A crystal structure. J Mol Biol 2004,
342:571-583.
54. Yeagle PL, Salloum A, Chopra A, Bhawsar N, Ali L, Kuzmanovski G,
Alderfer JL, Albert AD: Structures of the intradiskal loops and
amino terminus of the G-protein receptor, rhodopsin. J Pept
Res 2000, 55:455-465.
55. Nonato MC, Widom J, Clardy J: Crystal structure of the N-ter-

minal segment of human eukaryotic translation initiation
factor 2alpha. J Biol Chem 2002, 277:17057-17061.
56. Reyes-López CA, Hernández-Santoyo A, Pedraza-Escalona M, Men-
doza G, Hernández-Arana A, Rodríguez-Romero A: Insights into a
conformational epitope of Hev b 6.02 (hevein). Biochem Bio-
phys Res Commun 2004, 314:123-130.
57. Toyoshima C, Nomura H, Tsuda T: Lumenal gating mechanism
revealed in calcium pump crystal structures with phosphate
analogues. Nature 2004, 432:361-368.
58. Kühlbrandt W, Zeelen J, Dietrich J: Structure, mechanism, and
regulation of the Neurospora plasma membrane H+-
ATPase. Science 2002, 297:1692-1696.
59. Yoon S, Welsh WJ: Detecting hidden sequence propensity for
amyloid fibril formation. Protein Sci 2004, 13:2149-2160.
60. The Dali Database [ />61. Laskowski RA, Rullmannn JA, MacArthur MW, Kaptein R, Thornton
JM: AQUA and PROCHECK-NMR: programs for checking
the quality of protein structures solved by NMR. J Biomol NMR
1996, 8:477-486.
62. Camon E, Magrane M, Barrell D, Lee V, Dimmer E, Maslen J, Binns D,
Harte N, Lopez R, Apweiler R: The Gene Ontology Annotation
(GOA) Database: sharing knowledge in Uniprot with Gene
Ontology. Nucleic Acids Res 2004, 32(Database
issue):D262-D266.
63. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local
alignment search tool. J Mol Biol 1990, 215:403-410.
64. UniProt []
65. Schneider TD, Stephens RM: Sequence logos: a new way to dis-
play consensus sequences. Nucleic Acids Res 1990, 18:6097-6100.
66. Hubbard S: NACCESS: a Program for Calculating Accessibili-
ties. In PhD thesis University College of London, Department of Bio-

chemistry and Molecular Biology; 1992.
67. McDonald IK, Thornton JM: Satisfying hydrogen bonding poten-
tial in proteins. J Mol Biol 1994, 238:777-793.
68. Camon E, Barrell D, Lee V, Dimmer E, Apweiler R: The Gene
Ontology Annotation (GOA) Database - an integrated
resource of GO annotations to the UniProt Knowledgebase.
In Silico Biol 2004, 4:5-6.
69. Barton GJ: ALSCRIPT: a tool to format multiple sequence
alignments. Protein Eng 1993, 6:37-40.